Field-based Crop Mapping through Sequential Masking Classification of Multi-temporal Landsat-7 Etm+ Images in Karacabey, Turkey

This study presents a field-based crop mapping through sequential masking classification of multi-temporal Landsat-7 ETM+ images acquired in May, July, and August 2000 in Karacabey, Turkey. First, the classification of each image date was carried out on a standard per pixel basis. The results of the per pixel classification were integrated with digital agricultural field boundaries and for each field, a crop type was determined based on the modal crop class calculated within the field. The classification accuracy was computed by comparing the reference data, field-by-field, to each classified image. The individual crop accuracies were examined on each classified data to determine those crops whose accuracy exceeds a preset threshold level. Then, the multi-temporal masking classification of the crops was carried out in sequential steps using the three image dates, excluding after each classification the crop properly classified. The masking technique was applied to overcome the problems caused by the spectral overlaps between some classes. The final classified data was analyzed in a field specific manner to assign each field a crop label. An immediate update of the database was provided by directly entering the results of the analysis into the database. The use of sequential masking procedure for field-based crop mapping improved the overall accuracies of the classifications of the July and August images alone by more than 10%.